Integrating Correlation Clustering and Agglomerative Hierarchical Clustering for Holistic Schema Matching

Alshaikhdeeb, Basel; Ahmad, Kamsuriah

doi:10.3844/jcssp.2015.484.489

Cited by 8 publications

(11 citation statements)

References 15 publications

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“…Holistic schema matching is the process of extracting correspondences among large-scale web interfaces repository [8]. The key challenge behind any schema matching approach lies on its effectiveness of matching results, especially when handling large-scale data.…”

Section: Methodsmentioning

confidence: 99%

“…Whereas, hierarchical produces effective results, but restricted due to its time complexity [6]. Additionally, some approaches have been integrated different clustering techniques in order to get better results [7,8].…”

Section: Introductionmentioning

confidence: 99%

“…It achieves an 89% of accuracy for Airfare dataset. Similarly, Alshaikhdeeb & Ahmad [8] has proposed an integration of correlation clustering and agglomerative hierarchical clustering for holistic schema matching. The proposed method has been carried out on the Airfare dataset.…”

Section: Introductionmentioning

confidence: 99%

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Schema Matching for Large-Scale Data Based on Ontology Clustering Method

Alani

Saad

2017

International Journal on Advanced Science, Engineering and Information Technology

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Holistic schema matching is the process of identifying semantic correspondences among multiple schemas at once. The key challenge behind holistic schema matching lies in selecting an appropriate method that has the ability to maintain effectiveness and efficiency. Effectiveness refers to the quality of matching while efficiency refers to the time and memory consumed within the matching process. Several approaches have been proposed for holistic schema matching. These approaches were mainly dependent on clustering techniques. In fact, clustering aims to group the similar fields within the schemas in multiple groups or clusters. However, fields on schemas contain much complicated semantic relations due to schema level. Ontology which is a hierarchy of taxonomies has the ability to identify semantic correspondences with various levels. Hence, this study aims to propose an ontology-based clustering approach for holistic schema matching. Two datasets have been used from ICQ query interfaces consisting of 40 interfaces, which refer to Airfare and Job. The ontology used in this study has been built using the XBenchMatch which is a benchmark lexicon that contains rich semantic correspondences for the field of schema matching. In order to accommodate the schema matching using the ontology, a rule-based clustering approach is used with multiple distance measures including Dice, Cosine, and Jaccard. The evaluation has been conducted using the common information retrieval metrics; precision, recall, and f-measure. In order to assess the performance of the proposed ontology-based clustering, a comparison among two experiments has been performed. The first experiment aims to conduct the ontology-based clustering approach (i.e. using ontology and rule-based clustering), while the second experiment aims to conduct the traditional clustering approaches without the use of ontology. Results show that the proposed ontology-based clustering approach has outperformed the traditional clustering approaches without ontology by achieving an fmeasure of 94% for Airfare and 92% for Job datasets. This emphasizes the strength of ontology in terms of identifying correspondences with semantic level variation.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Schema Matching for Large-Scale Data Based on Ontology Clustering Method

Alani

Saad

2017

International Journal on Advanced Science, Engineering and Information Technology

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“…Clustering can be performed using two main approaches; partitioning (e.g., k-means clustering) and hierarchical clustering (e.g., agglomerative clustering) [6]. Each clustering technique is integrated with a particular similarity (distance) measure that could identify similarity among the objects [7]. Furthermore, finding an appropriate similarity measure that is suitable for the clustering technique is a challenging task [8].…”

Section: Introductionmentioning

confidence: 99%

Modified Dynamic Time Warping for Hierarchical Clustering

Sammour

Othman

Rus

et al. 2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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Time series clustering is the process of grouping sequential correspondences in similar clusters. The key feature behind clustering time series data lies on the similarity/distance function used to identify the sequential matches. Dynamic Time Warping (DTW) is one of the common distance measures that have demonstrated competitive results compared to other functions. DTW aims to find the shortest path in the process of identifying sequential matches. DTW relies on dynamic programming to obtain the shortest path where the smaller distance is being computed. However, in the case of equivalent distances, DTW is selecting the path randomly. Hence, the selection could be misguided in such randomization process, which significantly affects the matching quality. This is due to randomization may lead to the longer path which drifts from obtaining the optimum path. This paper proposes a modified DTW that aims to enhance the dynamic selection of the shortest path when handling equivalent distances. Experiments were conducted using twenty UCR benchmark datasets. Also, the proposed modified DTW result has been compared with the state of the art competitive distance measures which is based on precision, recall and f-measure including the original DTW, Minkowski distance measure and Euclidean distance measure. The results showed that the proposed modified DTW reveal superior results in compared to the standard DTW, either using Minkowski or Euclidean. This can demonstrate the effectiveness of the proposed modification in which optimizing the shortest path has enhanced the performance of clustering. The proposed modified DTW can be used for having good clustering method for any time series data.

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“…With the dramatic expansion of information over web, nowadays, numerous websites are providing vast amount of source code to support developers (Alshaikhdeeb and Ahmad, 2015). Splitting identifiers is a task that has been addressed in the past few years in order to contribute toward improving the Feature Location task.…”

Section: Introductionmentioning

confidence: 99%

A Backpropagation Neural Network for Splitting Identifiers

AL-Jumaili¹,

Tayyeh²,

Kadhem³

2018

Journal of Computer Science

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Splitting identifiers is a task that has been addressed in the past few years in order to contribute toward improving the Feature Location task. Feature Location aims at determining the exact position of a specific feature within a source code. Several research studies have addressed the process of splitting multi-word identifiers. However, one of the endure gaps that still face the use of machine learning lies on using probabilistic algorithms which may seem insufficient compared to other sophisticated algorithms such as the Backpropagation Neural Network (BPNN). Therefore, this paper proposes a BPNN for the splitting identifiers task. A benchmark of source code dataset has been used in the experiments. In addition, different objective functions have been used including Tanh, Sigmoid and Softmax. Results showed that Softmax has outperformed the other objective funciton by achieving a 71.4% of f-measure. This results implies the usefulness of BPNN in terms of handling character-based problems.

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Integrating Correlation Clustering and Agglomerative Hierarchical Clustering for Holistic Schema Matching

Cited by 8 publications

References 15 publications

Schema Matching for Large-Scale Data Based on Ontology Clustering Method

Schema Matching for Large-Scale Data Based on Ontology Clustering Method

Modified Dynamic Time Warping for Hierarchical Clustering

A Backpropagation Neural Network for Splitting Identifiers

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